To this end, we aim at studying the fairness implications of yet
another DP mechanism for training deep neural networks, named PATE [1]}. In PATE (Private Aggregation of Teacher Ensembles), sensitive data is split into a certain number of disjoint training sets, on each of which a teacher classifier is trained. Then, a student model is trained by transferring the noisy aggregate knowledge of the teachers. The noisy aggregation is where the privacy guarantees are induced through DP.
